Shuffled Frog Leaping Algorithm Research Articles

Optimal Dynamic Scheduling of Electric Vehicles in a Parking Lot Using Particle Swarm Optimization and Shuffled Frog Leaping Algorithm

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.

Fuzzy multi-hop clustering protocol: Selection fuzzy input parameters and rule tuning for WSNs

Nowadays, the most important aspects of wireless sensor networks (WSNs) are to make optimal use of and direct the limited energy of sensor nodes towards the desired application and prolong the network lifetime for that application. Although a few studies exist that have addressed these special goals, they have been mostly focused on the process of selecting cluster heads (CHs) and forwarders. No studies have been conducted so far on the selection of fuzzy input parameters in clustering and routing processes as well as the application-based parameter selection process. Generally, a fixed number of parameters have always been selected by designers. Hence, the shuffled frog leaping algorithm (SFLA) was employed in this paper to propose a technique for selecting fuzzy input parameters in a fuzzy multi-hop clustering protocol named the PS-SFLA. This technique includes three main phases, introduced in three versions for the sake of stepwise evaluation. Based on the literature review, the most frequent and diverse parameters were extracted and formulated in the first version. The proposed technique used the SFLA in the second version to select the appropriate parameters fitting the application specifics and scenario and determine the coefficients of parameters simultaneously so that they could be used as the inputs of the fuzzy inference system. It was also utilized in the final version for the automated, accurate, application-based tuning of fuzzy rules before the network was set up. By design, different versions of the PS-SFLA act as the starting points of the next version in addition to the fact that they can be evaluated separately. The PS-SFLA was compared with the LEACH, ASLPR, SIF, ERA, and FSFLA in different scenarios and two applications from the perspective of the alive nodes, the number of packets received by the BS, lifetime, and other factors. According to the simulation results, the PS-SFLA outperformed all of the other methods greatly in all scenarios and applications and PS-SFLA increases the lifetime due to the appropriate selection of fuzzy input parameters based on application and purpose.

A review of applications of animal‐inspired evolutionary algorithms in reservoir operation modelling

AbstractSuccessful operation of reservoir systems to guarantee the optimal use of available water resources has been the subject of many studies. The advent and applications of evolutionary algorithms (EAs) in the field of reservoir operation have led to significant advances in our capacity to improve the planning and management of complex reservoir systems. This study reports a review of the applications of animal‐inspired EAs to reservoir operation optimization selected among a large number of available papers in this area of research. The animal‐inspired EAs herein identified concern algorithms that mimic biologic traits of animal (wild) species. Among the animal‐inspired EAs ant colony optimization (ACO), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA), artificial bee colony (ABC), honey bee mating optimization (HBMO), firefly algorithm (FA), cuckoo search (CS) and the bat algorithm (BA) are the best‐known ones selected for this review. This paper presents a brief description of the algorithmic characteristics and various employed improved versions or varieties thereof of each of the stated EAs. Furthermore, the differences between the proposed animal‐inspired EAs and their improved versions are identified by comparing the performance of the implemented animal‐inspired EAs in the reviewed literature. PSO and its varieties have the largest number of reported applications. Our comparison results revealed that constrained, discrete and randomized varieties of the animal‐inspired EAs outperformed unconstrained, continuous and deterministic varieties, respectively because of larger feasible search space, better solution quality and shorter computational time. Moreover, all the animal‐inspired EAs outperformed traditional methods of reservoir optimization, such as nonlinear programming (NLP) and dynamic programming (DP).

Model optimization and parameter sensitivity analysis of a parabolic composite section open channel*

AbstractIn channel optimization, the objective function and the oft‐used trapezoidal cross‐section channel in excavation models are not comprehensive. The parabolic composite section channel is a new type of channel with a parabolic slope and a horizontal bottom. To improve channel optimization, we propose models of a parabolic composite section channel constructed with excavation, filling, and half‐filling. We establish three parabolic composite cross‐section channel models that consider freeboard, and we establish three construction methods. The objective is to minimize the combined cost of excavation, filling, and concrete lining, and the objective function covers all construction methods and construction costs. To avoid missing the feasible optimal solution, the shuffled frog‐leaping algorithm (SFLA) is used to optimize the three channel models. An objective function within the acceptable range of parameter variation is represented by a MATLAB 3D rainbow graph, and sensitivity analyses of various section parameters are carried out. The results show that the model constructed with excavation has the lowest investment cost. The bottom width of the parabolic composite channel is 1.74 m, the channel top width is 5.69 m, the channel depth is 3.22 m, the channel excavation section area is 14.1 m2, the channel section lining length is 9.62 m, and the minimum cost per kilometre is 591,000 yuan. SFLA is superior to the differential evolution, artificial bee colony, and particle swarm optimization algorithms in terms of convergence speed and optimization accuracy, and the objective function is the least sensitive to the opening width of the parabola.

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders.

The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine–Radial Basis Function (SVM–RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.

Comprehensive Analysis of PAPR Reduction in OFDM, UFMC and Signal Scrambling UFMC Technique using Shuffled Frog Leaping Algorithm

Universal Filter Multi-Carrier (UFMC) is one of the most recommended waveform designs for the next-generation wireless communication system. UFMC provides high spectral efficiency, better sub-carrier separation with less PAPR when compared to OFDM. The major drawback of UFMC is that it suffers from a significant peak to average power ratio similar to multi-carrier communication techniques like Orthogonal Frequency Division Multiplexing (OFDM). The research gap is addressed by proposing the signal scrambling technique to reduce the Peak Average Power Ratio(PAPR) in UFMC. A Shuffled Frog Leaping Algorithm (SFLA) is used as an optimizer to verify the entire search space for optimal phase vectors within the time constraints to produce the best-modified UFMC signal with less PAPR. An analogy between OFDM, UFMC and signal-scrambling UFMC is performed, and it is observed that the signal-scrambled UFMC produces better results than general UFMC and OFDM in terms of PAPR.

A Biologically Inspired ELM-based Framework for Classification of Brain MRIs

<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Use of medical images for clinical analysis of various critical diseases have become increasingly predominant in modern health care systems. Application of machine learning technique in this context evolves as a potential solution in terms providing faster output with high diagnostic accuracy. In this work we propose an Extreme Learning Machine (ELM) based classifier SFLA-ELM for detection of normal and pathological brain condition from brain Magnetic Resonance Images (MRIs). ELM is known for its speed and accuracy whereas the proposed method uses a swarm based evolutionary technique Shuffled Frog Leaping Algorithm (SFLA) and 10-fold cross validation method to optimally determine the network parameter of the ELM for better classification performance.</span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Droid Sans Fallback'; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> The proposed model is experimented on three different brain MRI datasets of three different brain diseases. </span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">To get better approximation accuracy and generalization ability for the base ELM classifier, the suitable activation function and the appropriate number of hidden layer nodes are chosen. The performance validation of the proposed framework is done under two different network conditions, i.e. fixed network structure and varying network structure, by comparing its performance with two standard hybridized ELM classifiers, namely, PSO-ELM and ABC-ELM. The comparative performance analysis suggests that the proposed SFLS-ELM gives better classification performance in diagnosing the diseases in terms of accuracy, sensitivity, specificity, F-score and Area under ROC curve (AUC).Furthermore, the SFLA-ELM also found to offer better generalization ability and better stability with more compact network structure.</span><span style="font-size: 22.0pt; mso-bidi-font-size: 16.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">A Biologically Inspired ELM-based Framework for Classification of Brain MRIs</span>

A Modified Shuffled Frog Leaping Algorithm for the Topology Optimization of Electromagnet Devices

The memetic algorithms which employ population information spreading mechanism have shown great potentials in solving complex three-dimensional black-box problems. In this paper, a newly developed memetic meta-heuristic optimization method, known as shuffled frog leaping algorithm (SFLA), is modified and applied to topology optimization of electromagnetic problems. Compared to the conventional SFLA, the proposed algorithm has an extra local search step, which allows it to escape from the local optimum, and hence avoid the problem of premature convergence to continue its search for more accurate results. To validate the performance of the proposed method, it was applied to solving the topology optimization of an interior permanent magnet motor. Two other EAs, namely the conventional SFLA and local-search genetic algorithm, were applied to study the same problem and their performances were compared with that of the proposed algorithm. The results indicate that the proposed algorithm has the best trade-off between the results of objective values and optimization time, and hence is more efficient in topology optimization of electromagnetic devices.

Metaheuristic-hybridized multilayer perceptron in slope stability analysis

This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.

A Diagnostic Method for Moisture Intrusion Fault in OIP Bushing

Oil-impregnated paper (OIP) bushing is essential equipment in power substations, and moisture intrusion is one of the major faults during its operation. In this article, a method is proposed to diagnose the moisture intrusion fault by the hidden Markov model (HMM) and the shuffled frog-leaping algorithm (SFLA). First, HMM is employed to characterize the probabilistic relationship between the moisture states and frequency-domain spectroscopy (FDS). Then, SFLA is adopted to optimize the locatable results of moisture intrusion point (MIP) in the bushing. Next, extensive simulated data are used for parameter training in the HMM-SFLA model library, with cross-validation for optimal parameters' configuration of HMM and SFLA. Finally, the proposed method was validated by experiment in laboratory and field measurements. The results show that the proposed method can effectively diagnose the MIP of OIP bushings.

Mendelian evolutionary theory optimization algorithm

This study presented a new multi-species binary coded algorithm, Mendelian evolutionary theory optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: first, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second, the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimutation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators—(1) Flipper, (2) Pollination, (3) Breeding, and (4) Epimutation—are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers: (1) binary hybrid GA, (2) bio-geography-based optimization, (3) invasive weed optimization, (4) shuffled frog leap algorithm, (5) teaching–learning-based optimization, (6) cuckoo search, (7) bat algorithm, (8) gravitational search algorithm, (9) covariance matrix adaptation evolution strategy, (10) differential evolution, (11) firefly algorithm and (12) social learning PSO. This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal–Wallis statistical rank-based nonparametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.

A reactive search optimization algorithm for scientific workflow scheduling using clustering techniques

Cloud computing is the style that can give plenty of shared pool resources such as hardware or software to clients based on requests from the internet. These resources are then scaled up automatically based on the specifications of the clients. Workflow scheduling optimization is an area of research activities in infrastructure as a service (IaaS) of the cloud. This problem is NP-complete. Thus, building a workflow scheduler that is optimum, having a reasonable level of performance and speed of computation, can be quite challenging in a distributed cloud environment. Metaheuristic algorithms may be improved in terms of their solution and its quality and speed of convergence utilizing combining it with other metaheuristic algorithms or any other algorithms that are metaheuristic based on local search. Shuffled frog leaping algorithm (SFLA) was acknowledged a metaheuristic performing heuristic search with a heuristic function (mathematical function) seeking solutions to combinatorial optimization problems. An optimization ratio on makespan %, resource utilization and computational cost performs better for SFLA–RSO with clustering when the number of tasks are increased.

A hybrid algorithm based on MOSFLA and GA for multi-UAVs plant protection task assignment and sequencing optimization

We propose a Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) and Genetic Algorithm (GA) based task assignment and sequencing method, for multi-Unmanned Aerial Vehicles (multi-UAVs) plant-protection operation optimization. Based on full coverage of spray area, a dual decision model of non-operation flight distance and total operation time is developed considering energy consumption and operation efficiency. The proposed optimization method is hybridized using MOSFLA and GA: we first use modified MOSFLA for multi-UAVs operation assignment optimization, shrinking multi-UAVs operation cost including fields allocation, non-operation flight distance and operation time difference; we then employ GA for fields operation sequencing optimization, reducing the total operation time. Considering multi-UAVs’ take-off preparation delay effect, we established a latency time calculation model to determine total operation time for multi-UAVs. The test results show that: ① the non-operation flight distance cost for multi-UAVs using MOSFLA is less than that of single-UAV, which also performs better than that for multi-UAVs in traditional modes; ② the total operation time shrinks by using GA with known assignment matrix (including MOSFLA), which could save over 20 min compared with traditional modes; ③ the total operation time cost in MOSFLA–GA is less than that in traditional modes and optimized traditional modes, with same UAV number and preparation time for take-off; ④ the minimum iteration when assignment fitness value of MOSFLA attains maximum is small (< 20); the minimum iteration when sequencing fitness value of GA attains maximum is less than 100, but is 5 in certain cases.

Crosstalk-Aware Global Routing in VLSI Design by Using a Shuffled Frog-Leaping Algorithm

Nowadays, very large scale integrated (VLSI) circuit technology is developing rapidly. It is necessary to consider many factors related to the VLSI circuit design. Interference is one of the factors that must be considered in high-frequency systems. The parasitic elements become serious limiting factors in the circuit. This research provided a method to reduce crosstalk energy by considering the transition of the signal. Crosstalk is the main capacitive effect which is elected by a high-coupling capacitance between lines. This study considers the wiring path signal with disturbance using the theory of optimization model, assisting in the search of the best sort in signal lines. The technique of a shuffled frog leaping algorithm (SFLA) is being used to search for the best value in arranged signal lines. The result will be minimal noise. The study finds that the arrangement using the SFLA causes only 36.42% of the noise. It was initially evident and 13.06%, when compared with the average all, is born noise value. These techniques can be applied in the design of arranging signal line in the VLSI circuits.

Optimization of fuzzy classifier parameters with a combination of gravitational search algorithm and shuffled frog leaping algorithm

In the present article, we analyse the effectiveness of combining two metaheuristic algorithms for tuning parameters of a fuzzy classifier. To work with imbalanced data, a fitness function is used based on a compromise between the overall accuracy and the geometric mean of accuracy of each class. The experiment was performed on data sets from the “Knowledge Extraction based on Evolutionary Learning” repository with different imbalance coefficients.

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing.

With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content.

Online Modeling of a Fuel Cell System for an Energy Management Strategy Design

An energy management strategy (EMS) efficiently splits the power among different sources in a hybrid fuel cell vehicle (HFCV). Most of the existing EMSs are based on static maps while a proton exchange membrane fuel cell (PEMFC) has time-varying characteristics, which can cause mismanagement in the operation of a HFCV. This paper proposes a framework for the online parameters identification of a PMEFC model while the vehicle is under operation. This identification process can be conveniently integrated into an EMS loop, regardless of the EMS type. To do so, Kalman filter (KF) is utilized to extract the parameters of a PEMFC model online. Unlike the other similar papers, special attention is given to the initialization of KF in this work. In this regard, an optimization algorithm, shuffled frog-leaping algorithm (SFLA), is employed for the initialization of the KF. The SFLA is first used offline to find the right initial values for the PEMFC model parameters using the available polarization curve. Subsequently, it tunes the covariance matrices of the KF by utilizing the initial values obtained from the first step. Finally, the tuned KF is employed online to update the parameters. The ultimate results show good accuracy and convergence improvement in the PEMFC characteristics estimation.

A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems

Multi-objective optimization is many important since most of the real world problems are in multi-objective category. Looking at the literature, the algorithms proposed for the solution of multi-objective problems have increased in recent years, but there is no a convenient approach for all kind of problems. Therefore, researchers aim to contribute to the literature by offering new approaches. In this study, an algorithm based on gray wolf optimizer (GWO) with memeplex structure of the shuffled frog leaping algorithm (SFLA), which is named as multi-objective shuffled GWO (MOSG), is proposed to solve the multi-objective optimization problems. Additionally, some modifications are applied on the proposed algorithm to improve the performance from different angles. The performance of the proposed algorithm is compared with the performance of six multi-objective algorithms on a benchmark set consist of 36 problems. The experimental results are presented with four different comparison metrics and statistical tests. According to the results, it can easily be said that the proposed algorithm is generally successful to solve the multi-objective problems and has better or competitive results.

Multi-population meta-heuristics for production scheduling: A survey

Abstract Production scheduling is one of the most critical issues in manufacturing and production systems and has markedly positive impact on the performances of manufacturing. In the past decades, various scheduling problems have been extensively solved by multi-population meta-heuristics, which are artificial bee colony (ABC), imperialist competitive algorithm (ICA) and shuffled frog-leaping algorithm (SFLA); however, the related works of multi-population meta-heuristics to scheduling are hardly reviewed. In this paper, we provide an extensive recall on solving production scheduling based on ABC, ICA or SFLA. A new classification on scheduling problems is first given, then three algorithms are described and their applications to scheduling are summarized in a systematic way; finally, the main conclusions are drawn and some future research directions are presented.

Hybrid genetic and shuffled frog‐leaping algorithm for neural network structure optimization and learning model to predict free spectrum in cognitive radio

SummaryCognitive radio, which is called an intelligent radio, will dynamically access the available spectrum. This mechanism will bring revolt in wireless communication, which lightens the spectrum utilization problem. Machine learning plays a vital role in every technology. Here, in cognitive radio, it helps train the model to predict the free spectrum. The cognitive clients utilize range‐detecting procedures to detect the groups previously transmitting on them to stay away from impact with the authorized clients that prompts deferral and moderates more vitality. To decrease postponement and vitality utilization and to foresee the future use of channels, range expectation procedures are utilized. Spectrum prediction is used to anticipate the future channel status in light of gathered chronicled information; here, to take care of the issue, the neural system‐based spectrum prediction utilizes a backpropagation training model that has been proposed. To enhance the structure of the neural system and to decrease the forceful weight auxiliary pattern, genetic algorithm (GA) along with the hybrid combination of shuffled frog‐leaping algorithm (SFLA) is proposed. Here, GA has been utilized to abstain from catching nearby ideal solutions. The selection, crossover, and mutation functions were performed to build the haphazardness, which stretches out the populace unite to the set that contains the global ideal solution. SFLA has been proposed for structure improvement; the paired structure has been recommended to demonstrate the memes with the motivation behind building up a subaccumulation with lesser measurements than that of the original group where recognizing affectability and precision would be versatile with that of the primary status. Simulation results show the GA‐SFLA‐based hybrid algorithm that is used has increased the results of getting the best weights by improving the system; additionally, the proposed conspire results show high forecast accuracy.